| /external/webp/src/enc/ |
| analysis_enc.c | 77 const int centers[NUM_MB_SEGMENTS],
80 int min = centers[0], max = centers[0];
85 if (min > centers[n]) min = centers[n];
86 if (max < centers[n]) max = centers[n];
92 const int alpha = 255 * (centers[n] - mid) / (max - min);
93 const int beta = 255 * (centers[n] - min) / (max - min);
146 // array bounds of 'centers' with some compilers (noticed with gcc-4.9)
149 int centers[NUM_MB_SEGMENTS]; local
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| clustering_ops.cc | 298 InvalidArgument("Input centers should be a matrix."));
316 const Eigen::Map<const MatrixXfRowMajor> centers(
339 0.5 * centers.rowwise().squaredNorm();
343 // sharding the points and centers as follows:
345 // 1. Centers are sharded such that each block of centers has at most
348 // block of centers. The block size of points is chosen such that the
352 // are reduced to a set of k nearest centers as soon as possible. This
369 num_centers) /* centers in a block */
372 // The memory needed for storing the centers being processed. This is share
[all...] |
| clustering_ops_test.cc | 35 // Number of centers for tests.
179 Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims}));
182 centers.flat<float>().setRandom();
187 .Input(test::graph::Constant(g, centers))
|
| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| gmm_ops_test.py | 46 self.centers = [[1, 1], [-1, 0.5], [2, 1]]
48 self.num_examples, self.centers)
73 def make_data_from_centers(num_vectors, centers):
74 """Generates 2-dimensional data with random centers.
78 centers: a list of random 2-dimensional centers.
86 current_class = np.random.random_integers(0, len(centers) - 1)
88 np.random.normal(centers[current_class][0],
90 np.random.normal(centers[current_class][1], np.random.random_sample())
93 return np.asarray(vectors), len(centers)
[all...] |
| kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20):
62 num_centers, num_dims = centers.shape
66 return (centers[assignments] + offsets, assignments, np.add.reduce(
221 # Make a call to fit to initialize the cluster centers.
381 centers = normalize(self.kmeans.cluster_centers())
382 centers = centers[centers[:, 0].argsort()]
384 self.assertAllClose(centers, true_centers, atol=0.04)
388 centers = normalize(self.kmeans.cluster_centers()
[all...] |
| gmm_test.py | 78 def make_random_points(centers, num_points):
79 num_centers, num_dims = centers.shape
83 points = centers[assignments] + offsets
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20):
62 num_centers, num_dims = centers.shape
66 return (centers[assignments] + offsets, assignments, np.add.reduce(
227 # Make a call to fit to initialize the cluster centers.
349 centers = normalize(self.kmeans.clusters())
350 centers = centers[centers[:, 0].argsort()]
352 self.assertAllClose(centers, true_centers, atol=0.04)
356 centers = normalize(self.kmeans.clusters()
[all...] |
| dynamic_rnn_estimator_test.py | 661 # Create examples by choosing 'centers' and adding uniform noise.
662 centers = math_ops.matmul(
672 sequences = centers + noise
|
| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| keras_image_model_correctness_test.py | 63 centers = np.random.randn(num_classes, *shape)
72 features.append(centers[label] + offset)
|
| /external/skia/src/shaders/gradients/ |
| SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; local
73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
|
| /external/skqp/src/shaders/gradients/ |
| SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; local
73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
|
| /cts/apps/CtsVerifier/src/com/android/cts/verifier/sensors/ |
| RVCVXCheckAnalyzer.java | 823 MatOfPoint2f centers = new MatOfPoint2f(); local
854 gray, patternSize, centers, Calib3d.CALIB_CB_ASYMMETRIC_GRID);
862 Calib3d.drawChessboardCorners(frame, patternSize, centers, true);
869 Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec,
884 Point[] centerPoints = centers.toArray();
892 // Calculate the average pixel error between the circle centers from the video and the
893 // reprojected circle centers based on the estimated camera position. The error provides
[all...] |
| /external/libjpeg-turbo/simd/i386/ |
| jdsample-avx2.asm | 44 ; The upsampling algorithm is linear interpolation between pixel centers,
46 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
47 ; of the way between input pixel centers.
|
| jdsample-mmx.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; of the way between input pixel centers.
|
| jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; of the way between input pixel centers.
|
| /external/libjpeg-turbo/simd/x86_64/ |
| jdsample-avx2.asm | 44 ; The upsampling algorithm is linear interpolation between pixel centers,
46 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
47 ; of the way between input pixel centers.
|
| jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; of the way between input pixel centers.
|
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops_test.py | 411 # Expose cluster centers, i.e. medoids.
413 # Expose indices of chosen cluster centers.
521 n_samples=n_samples, centers=n_clusters)
|
| /external/tensorflow/tensorflow/go/op/ |
| wrappers.go | [all...] |
| /cts/apps/CtsVerifier/libs/ |
| opencv3-android.jar | |
